In the transmission of signals which are normally broadband, such as video and the like, it is sometimes desireable and expedient to reduce the bandwidth even though some definition of the data being transmitted is lost. Reducing the bandwidth, generally referred to as bandwidth compression, is performed by first transforming the data into a different domain, quantizing the transformed data and selecting terms with the highest energy. Generally, higher order terms contain less information and energy and lower order terms contain more information and energy. At the receiver the reverse operation is performed to transform the data back into the wideband, e.g., video.
The discrete cosine transform is similar to the discrete Fourier transform and has become an extremely popular transform for bandwidth compression techniques. It has been shown to be very close to the Karhunen-Loeve transform, which is optimal in producing uncorrelated coefficients. Bandwidth compression techniques where a two dimensional transformation is used usually require only an 8 or 16 point transform. To perform this transform previous techniques used were based on a fast Fourier transform approach. However, for 8 point transforms the fast Fourier transform approach doesn't offer any real advantage over the direct matrix computation and requires increased control circuitry. Previous direct matrix computations required extensive multiplication, requiring relatively complicated apparatus and extensive time.